
import os
import mysql.connector
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime, date
from os import getcwd, path
import plotly.express as px
import plotly.offline as pyo
pyo.init_notebook_mode()
import seaborn as sns
plt.style.use("seaborn")
sns.set_style("whitegrid", {'axes.grid' : False})

#Working directory
path = r"C:\Users\Usuario\Documents\2. Programas\GitHub\Data and programming II\final-project-final-project-adriana-juan"


#Opening MySQL access and find the dataset:
mydb = mysql.connector.connect(
  host = "35.214.185.93",
  user = "uqjzxtryqrr5g",
  password = "qz0xs0lucobl"
)

mycursor = mydb.cursor()

mycursor.execute("SHOW DATABASES")

for x in mycursor:
  print(x)

('information_schema',)
('dbhfgcileg5zjx',)


#Show tables:
mydb = mysql.connector.connect(
  host = "35.214.185.93",
  user = "uqjzxtryqrr5g",
  password = "qz0xs0lucobl",
  database = "dbhfgcileg5zjx"
)

dbhfgcileg5zjx_tables = pd.read_sql_query("SHOW TABLES FROM dbhfgcileg5zjx", mydb)
dbhfgcileg5zjx_tables


#Opening each table. Each table was one part of the survey the organization did. Each part was not applied at the same time:
mydb = mysql.connector.connect(
  host = "35.214.185.93",
  user = "uqjzxtryqrr5g",
  password = "qz0xs0lucobl",
  database = "dbhfgcileg5zjx"
)

corona_table = "SELECT * FROM corona_details"
demographic_table = "SELECT * FROM demographic_info"
education_table = "SELECT * FROM education"
education_type_table = "SELECT * FROM education_type"
employment_table = "SELECT * FROM employment"
graduates_table = "SELECT * FROM graduates_followup"
lessons_followup_table = "SELECT * FROM lessons_followup"
lessons_mapping_table = "SELECT * FROM lessons_mapping"
users_table = "SELECT * FROM users"


#Lets start with the Users table to understand how they present each user:
df_users = pd.read_sql_query(users_table, mydb)
df_users["submit_date"] = pd.to_datetime(df_users["submit_date"], unit="s")
df_users
#We can see that une user its shown as shecodes_id and userid_connect. It was mentioned during a meeting. In some tables
#they present one user with shecodes_id and in others with userid_connect. For example, "Maria" can be shecodes_id = 5 
#and userid_connect=7


#Lets start with the Corona table:
df_corona = pd.read_sql_query(corona_table, mydb)
df_corona
#Here, it is shown users with shecodes_id. However, we observe that are repeated. This is because of how the survey was made.
#Big problem through all the project trying to map users. 

#For the Corona survey, we defined Coronavirus victims: an user who at least once was affected by COVID-19. It means that 
#if one user strugguled to find a job after her BA degree (because of COVID), but later she said that she could find a job,
#we say she is a COVID-19 victim.
#We created the corona_victim column where it is 1 if at least once she reported being affected by COVID.
#Then, we cleaned users remaining the once if at least were affected by COVID-19.


#Changing format to numeric:
df_corona["after_BA_work_difficulties"] = pd.to_numeric(df_corona["after_BA_work_difficulties"])    
df_corona["not_working_because_corona"] = pd.to_numeric(df_corona["not_working_because_corona"])    
df_corona["working_terms_got_worse"] = pd.to_numeric(df_corona["working_terms_got_worse"])    
df_corona["hard_to_find_work"] = pd.to_numeric(df_corona["hard_to_find_work"])    
print (df_corona.dtypes)

id                              int64
shecodes_id                     int64
after_BA_work_difficulties    float64
not_working_because_corona    float64
working_terms_got_worse       float64
hard_to_find_work             float64
submit_date                    object
dtype: object


for col in df_corona.columns:
    print(col)

id
shecodes_id
after_BA_work_difficulties
not_working_because_corona
working_terms_got_worse
hard_to_find_work
submit_date


#Defining corona victim and cleaning the data to work with 1 user:
def conditions(s):
    if (s['after_BA_work_difficulties'] > 0) or (s['not_working_because_corona'] > 0) or (s['working_terms_got_worse'] > 0) or (s['hard_to_find_work'] > 0):
        return 1
    else:
        return 0
    
df_corona['corona_victim'] = df_corona.apply(conditions, axis=1)

df_corona_victim = df_corona.sort_values('corona_victim',ascending=True).drop_duplicates('shecodes_id',keep='last')
df_corona_victim


#Calculating percentage of corona victims:
corona_counts = df_corona_victim["corona_victim"].value_counts().rename_axis("corona_victim").reset_index(name="counts")
corona_counts["percent"] = (corona_counts["counts"] / 
                  corona_counts['counts'].sum()) * 100
corona_counts


#Plot percentage of COVID victims:
my_colors=["lightblue","lightgreen",
           "silver","green"]

fig_1 = corona_counts.groupby(["corona_victim"]).sum().plot(kind="pie", 
                                                         y="percent", 
                                                         figsize=(6,6),colors=my_colors, 
                                                         autopct="%1.1f%%",
                                                         explode=(0,0.15),
                                                         labels= ["COVID Victims", "No COVID Victims"],
                                                         ylabel="",
                                                         title = "SheCodes Users: percentage impacted by COVID-19")
fig_1.figure.savefig("fig_1.pdf")
fig_1

<AxesSubplot:title={'center':'SheCodes Users: percentage impacted by COVID-19'}>


#Let's work now with Demographics:
mydb = mysql.connector.connect(
  host = "35.214.185.93",
  user = "uqjzxtryqrr5g",
  password = "qz0xs0lucobl",
  database = "dbhfgcileg5zjx"
)


demographic_table = "SELECT * FROM demographic_info"

df_demographic = pd.read_sql_query(demographic_table, mydb)
df_demographic["submit_date"] = pd.to_datetime(df_demographic["submit_date"], unit="s")
df_demographic


#We had access to the metadata (but it was in Hebrew). We needed to open each .csv document in order to see what
#they meant with each column and to understand the code. We can see that they worked here with shecodes_id and that we
#didn't have duplicated users.

#Let's start with family_status:
df_family_status = "sc_family_status.csv"
df_family_status = pd.read_csv(os.path.join(path, df_family_status))
label_english = ["Single", "In a relationship", "Married", "Separated", "Divorcee", "Live separately", "Widow", "Not interested in answering"]
df_family_status["label_english"] = label_english
df_family_status


family_counts = df_demographic["family_status"].value_counts().rename_axis("family_status").reset_index(name="counts")
family_counts["percent"] = (family_counts["counts"] / 
                  family_counts["counts"].sum()) * 100
family_counts


fig_2 = family_counts[["percent"]].plot(kind='barh', title ="SheCodes Users: Family Status", figsize=(8, 10), 
                                        legend=True, fontsize=12, align='center', color='#86bf91', zorder=2, width=0.85)
sns.set_style("whitegrid", {'axes.grid' : False})
fig_2.set_xlabel("Percent (%)", fontsize=12, weight="bold", color="black")
fig_2.set_ylabel("Family Status", fontsize=12, weight="bold", color="black")
fig_2.set_title("SheCodes Users: Family Status", fontsize=14, color="black")
fig_2.set_yticklabels(["Single", "In a relationship", "Married", "Separated", "Divorcee", 
                       "Live separately", "Widow", "Not interested in answering"])

fig_2.get_legend().remove()
plt.savefig("fig_2.pdf")
plt.show()


#Now, let's see birth_country:
df_birth_country = "sc_birth_country.csv"
df_birth_country = pd.read_csv(os.path.join(path, df_birth_country))
label_english_2 = ["Unknown", "Israel", "Eastern Europe and the former Brahms", "Western Europe", "Asia and the East", "Africa", "North America", "Central and South America", "Australia", "Middle East"]
df_birth_country["label_english_2"] = label_english_2
df_birth_country


country_counts = df_demographic["birth_country"].value_counts().rename_axis("birth_country").reset_index(name="counts")
country_counts["percent"] = (country_counts["counts"] / 
                  country_counts["counts"].sum()) * 100
country_counts


fig_3 = country_counts[["percent"]].plot(kind='barh', title ="SheCodes Users: Birth Country", 
                                         figsize=(8, 10), legend=True, fontsize=12, align='center', color='#86bf91', zorder=2, width=0.85)
sns.set_style("whitegrid", {'axes.grid' : False})
fig_3.set_xlabel("Percent (%)", fontsize=12, weight="bold", color="black")
fig_3.set_ylabel("Birth Country", fontsize=12, weight="bold", color="black")
fig_3.set_yticklabels(["Israel", "Eastern Europe and the former Brahms", "North America", "Western Europe", "Central and South America", "Africa", "Asia and the East", "Australia", "Middle East", "Unknown"])
fig_3.set_title("SheCodes Users: Birth Country", fontsize=14, color="black")
fig_3.get_legend().remove()
plt.savefig("fig_3.pdf")
plt.show()


#Let's see religious_level:
df_religious_level = "sc_religios_level_fields.csv"
df_religious_level = pd.read_csv(os.path.join(path, df_religious_level))
religious_level = ["Secular, not religious", "Traditional", "Religious", "Traditional - not so religious", "Traditional - religious", "Orthodox", "Haredi", "Not interested in answering", "Other"]
df_religious_level["religious_level"] = religious_level
df_religious_level


religious_counts = df_demographic["religios_level"].value_counts().rename_axis("religious_level2").reset_index(name="counts")
religious_counts["percent"] = (religious_counts["counts"] / religious_counts["counts"].sum()) * 100
religious_counts


religious_counts = religious_counts.merge(df_religious_level, how="inner", left_on="religious_level2", right_on="value")
religious_counts


fig_4 = sns.barplot(x = "religious_level",
            y = "percent",
            data = religious_counts,
            palette = "Greens")
sns.set_style("whitegrid", {'axes.grid' : False})
fig_4.set_xticklabels(fig_4.get_xticklabels(), rotation=40, ha="right")
fig_4.set_title("SheCodes Users: Religious Level", fontsize=14, color="black")
fig_4.set_xlabel("Religious Level", fontsize=12, color="black")
fig_4.set_ylabel("Percent", fontsize=12, color="black")
plt.tight_layout()
plt.savefig("fig_4.pdf")
plt.show()


#Calculating Age:
df_demographic.dtypes

id                                      int64
shecodes_id                             int64
birthdate                              object
family_status                         float64
kids_under_18                          object
kids_6-9                               object
kids_under_5                           object
birth_country                          object
aliya_year                             object
city_grow                              object
city                                   object
sector                                  int64
religios_level                        float64
is_army_or_ns                          object
is_army_or_ns_tech_unit                object
is_army_or_ns_tech_position            object
entry_date                             object
submit_date                    datetime64[ns]
dtype: object


df_demographic["birthdate"] = pd.to_datetime(df_demographic["birthdate"], errors="coerce")
df_demographic.dtypes

id                                      int64
shecodes_id                             int64
birthdate                      datetime64[ns]
family_status                         float64
kids_under_18                          object
kids_6-9                               object
kids_under_5                           object
birth_country                          object
aliya_year                             object
city_grow                              object
city                                   object
sector                                  int64
religios_level                        float64
is_army_or_ns                          object
is_army_or_ns_tech_unit                object
is_army_or_ns_tech_position            object
entry_date                             object
submit_date                    datetime64[ns]
dtype: object


today = pd.to_datetime("today")
df_demographic["Age"] = today.year - df_demographic["birthdate"].dt.year
df_demographic


#Understanding Age of Users: let's start with some exploratory data analysis.
#Source: https://medium.com/nerd-for-tech/analyse-the-distribution-of-ages-python-data-analysis-series-part-1-cc0fb2ca7f36
fig_5 = px.scatter(df_demographic, y="Age",  color_discrete_sequence =['green']*len(df_demographic))
fig_5.update_layout({"plot_bgcolor": "rgba(03, 0, 0, 0)",
                  "paper_bgcolor": "rgba(0, 0, 0, 0)",})
fig_5.show()


#Because we saw that there are some outliers (maybe errors from users when filled the survey), we filter the age:
df_demographic = df_demographic.query("(Age >= 10) and (Age <= 80)")
fig_6 = px.scatter(df_demographic, y="Age",  color_discrete_sequence =['green']*len(df_demographic))
fig_6.update_layout({"plot_bgcolor": "rgba(0, 0, 0, 0)",
                  "paper_bgcolor": "rgba(0, 0, 0, 0)",})
fig_6.show()


#Now that we have a distribution that seems right, lets see the age of our users in a better graph:
age_counts = df_demographic['Age'].value_counts()

fig_7 = px.bar(age_counts, title="SheCodes users: Age", color_discrete_sequence =['green']*len(age_counts))
fig_7.update_layout(
    xaxis_title = "Age",
    yaxis_title = "Frequency",
    title_x = 0.5, 
    showlegend = False)
fig_7.update_layout({"plot_bgcolor": "rgba(0, 0, 0, 0)",
                  "paper_bgcolor": "rgba(0, 0, 0, 0)",})
fig_7.write_image("fig_5.pdf") 
fig_7.show()
#We can see the distribution of user's age in the following graph (with a concentration
#around 36 years old)


#Let's start working with Education, Employment, and Graduates

connection = mysql.connector.connect(host = '35.214.185.93', 
                                     user = 'uqjzxtryqrr5g',
                                    password = 'qz0xs0lucobl',
                                    db = 'dbhfgcileg5zjx')
tables = pd.read_sql_query('SHOW TABLES FROM dbhfgcileg5zjx', connection)
employment_table = 'SELECT * FROM employment'
education_table = 'SELECT * FROM education'
education_type_table = 'SELECT * FROM education_type'
users_table = 'SELECT * FROM users'
graduates_table = 'SELECT * FROM graduates_followup'

df_employment = pd.read_sql_query(employment_table, connection)
df_education = pd.read_sql_query(education_table, connection)
df_education_type = pd.read_sql_query(education_type_table, connection)
df_users = pd.read_sql_query(users_table, connection)
df_graduates = pd.read_sql_query(graduates_table, connection)


df_education


## Solution for education date

df_graduates['date_enrolled'] = pd.to_datetime(df_graduates['date_enrolled'], unit='s')
df_graduates['date_graduated'] = pd.to_datetime(df_graduates['date_graduated'], unit='s')


## Organizing education type 3

df_1 = df_education.loc[df_education["type"] == 3]
df_1 = df_1.sort_values(by = "shecodes_id")
df_1 = df_1.sort_values('entry_date').drop_duplicates('shecodes_id',keep='last')


df_2 = df_education.loc[df_education["type"] == 2]
df_2 = df_2.sort_values(by = "shecodes_id")
df_2 = df_2.sort_values('entry_date').drop_duplicates('shecodes_id',keep='last')
df_2


df_3 = df_education.loc[df_education["type"] == 1]
df_3 = df_3.sort_values(by = "shecodes_id")
df_3 = df_3.sort_values('entry_date').drop_duplicates('shecodes_id',keep='last')
df_3


df_4 = df_education.loc[df_education["type"] == 4]
df_4 = df_4.sort_values(by = "shecodes_id")
df_4 = df_4.sort_values('entry_date').drop_duplicates('shecodes_id',keep='last')
df_4


frames = [df_1, df_2, df_3, df_4]
result = pd.concat(frames)
result_count = result["shecodes_id"].value_counts()


df_education_fin = pd.get_dummies(result, columns=['type']).groupby(['shecodes_id'], as_index=False).sum()
df_education_fin


df_employment


df_employment = df_employment.sort_values('entry_date').drop_duplicates('shecodes_id', keep='last')
df_employment.groupby(by = 'currently_employed')["shecodes_id"].count()

currently_employed
0.0    4757
1.0    4221
2.0      42
Name: shecodes_id, dtype: int64


df_employment


df_graduates


df_graduates_1 = df_graduates.sort_values(by = "date_enrolled").drop_duplicates(subset=['userid_connect'], keep='last')
df_graduates_1


df_grad_1 = df_users.merge(df_graduates_1, on="userid_connect", how="inner")
df_grad_1 = df_grad_1[["shecodes_id","userid_connect", "track_id_connect", "graduation_type",
                "date_enrolled", "date_graduated"]]


# Final merge with students from the Python course. Graduates and non-graduates. 

df_grad_1 = df_grad_1[["shecodes_id","userid_connect", "track_id_connect", "graduation_type",
                "date_enrolled", "date_graduated"]]


df_educ_back = df_grad_1.merge(df_education_fin, on = "shecodes_id", how = "inner")
df_educ_back = df_educ_back[["shecodes_id","userid_connect", "track_id_connect", "graduation_type",
            "date_enrolled", "date_graduated", "type_1.0","type_2.0", "type_3.0" ,"type_4.0" ]]


def cat_educ(x):
    # High School only
    if x["type_1.0"] == 1 and x["type_2.0"] == 0 and x["type_3.0"] == 0 and x["type_4.0"] == 0:
        return 1
    # Higher education with tech background
    elif x["type_3.0"] == 1 and x["type_2.0"] == 1:
        return 2
    # High school with tech background
    elif x["type_1.0"] == 1 and x["type_3.0"] == 0 and x["type_2.0"] == 1:
        return 3
    # Higher Education only 
    elif x["type_3.0"] == 1 and x["type_2.0"] == 0 and x["type_4.0"] == 0: 
        return 4
    # Other
    else: 
        return 0


## Situation that we want to show in the graph 
df_educ_back["status"] = df_educ_back.apply(lambda x: cat_educ(x), axis = 1)


###TRYING TO DO IT WITH GRAD


#Before, I had in 3 "80% Complete", and in 4: 100% Complete

graduation_type_dict = {2: "Incomplete", 3:"Complete", 4:"Complete"}
df_educ_back["graduation_type"].replace(graduation_type_dict, inplace = True)

status_dict = {0: "Other", 1:"High School only", 2:"Higher education with tech background",
               3:"High school with tech background", 4:"Higher Education only "}
df_educ_back["status"].replace(status_dict, inplace = True)
df_educ_back


graduation_level_counts = df_educ_back["status"].value_counts().rename_axis("status2").reset_index(name="counts")
graduation_level_counts["percent"] = (graduation_level_counts["counts"] / graduation_level_counts["counts"].sum()) * 100
graduation_level_counts


fig_11 = graduation_level_counts[["percent"]].plot(kind='barh', title ="SheCodes Users: Education background", figsize=(8, 10), 
                                        legend=True, fontsize=12, align='center', color='#86bf91', zorder=2, width=0.85)
sns.set_style("whitegrid", {'axes.grid' : False})
fig_11.set_xlabel("Percent (%)", fontsize=12, weight="bold", color="black")
fig_11.set_ylabel("Education background", fontsize=12, weight="bold", color="black")
fig_11.set_title("SheCodes Users: Education background", fontsize=14, color="black")
fig_11.set_yticklabels(["Higher Education only", "Higher education with tech background", "High School only", "Other", 
                        "High school with tech background"])

fig_11.get_legend().remove()
plt.savefig("fig_11.pdf")
plt.show()


agg_educ = df_educ_back.groupby(['graduation_type', "status"])["shecodes_id"].count().unstack()

sns.set_style("whitegrid", {'axes.grid' : False})
plt.figure(1)
agg_educ.plot(kind='bar', stacked=True)
plt.title('Composition of education by graduation level', fontweight='bold', color = 'black', fontsize='14', horizontalalignment='center')
plt.xticks(rotation=0, ha='center')
plt.xlabel('Graduation Type', fontweight='bold', color = 'black', fontsize='12', horizontalalignment='center')
plt.ylabel("Number of students", fontweight='bold', color = 'black', fontsize='12', horizontalalignment='center')
plt.legend(loc='upper left')
plt.savefig("fig_6.pdf")
plt.show()

<Figure size 576x396 with 0 Axes>


df_employ_fin = df_employment.merge(df_grad_1, how = "inner", on = "shecodes_id")
df_employ_fin = df_employ_fin[["shecodes_id", "currently_employed", "is_tech_company", "is_tech_position", 
              "graduation_type", "position_scope"]]
df_employ_fin = df_employ_fin[df_employ_fin["currently_employed"] != 2]


df_employ_fin


graduation_type_dict = {2: "Incomplete", 3:"Complete", 4:"Complete"}
df_employ_fin["graduation_type"].replace(graduation_type_dict, inplace = True)


employ_dict = {0: "Unemployed", 1:"Employed"}
df_employ_fin["currently_employed"].replace(employ_dict, inplace = True)

agg_employ = df_employ_fin.groupby(['graduation_type', "currently_employed"])["shecodes_id"].count().unstack().fillna(0)

sns.set_style("whitegrid", {'axes.grid' : False})
plt.figure()
plt.rcParams["figure.figsize"] = [6.00, 8.00]
agg_employ.plot(kind='bar', stacked=True)
plt.title('Composition of employment level by graduation type', fontweight='bold', color = 'black', fontsize='14', horizontalalignment='center')
plt.xticks(rotation=0, ha='center')
plt.xlabel('Graduation Type', fontweight='bold', color = 'black', fontsize='12', horizontalalignment='center')
plt.ylabel("Number of students", fontweight='bold', color = 'black', fontsize='12', horizontalalignment='center')
plt.legend(loc='upper left')
plt.savefig("fig_7.pdf")
plt.show()

<Figure size 576x396 with 0 Axes>


df_employ_fin


df_employ_fin_counts = df_employ_fin["currently_employed"].value_counts().rename_axis("currently_employed2").reset_index(name="counts")
df_employ_fin_counts["percent"] = (df_employ_fin_counts["counts"] / df_employ_fin_counts["counts"].sum()) * 100
df_employ_fin_counts


my_colors=["lightblue","lightgreen",
           "silver","green"]

fig_12 = df_employ_fin_counts.groupby(["currently_employed2"]).sum().plot(kind="pie", 
                                                         y="percent", 
                                                         figsize=(6,6),colors=my_colors, 
                                                         autopct="%1.1f%%",
                                                         explode=(0,0.15),
                                                         labels= ["Employed", "Unemployed"],
                                                         ylabel="",
                                                         title = "SheCodes Users: Employment Status")
fig_12.figure.savefig("fig_12.pdf")
fig_12

<AxesSubplot:title={'center':'SheCodes Users: Employment Status'}>


df_employ_fin["position_scope"] = pd.to_numeric(df_employ_fin["position_scope"])
position_scope_dict = {1: "Part-time (less than 50%)", 
                       2:"Part-time (over 50%)", 
                       3: "Full-time (over35 hours per week)", 
                       4: "Partially employed", 
                       5: "Freelance / Independent"}
df_employ_fin["position_scope"].replace(position_scope_dict, inplace = True)


df_employ_fin


agg_employ_2 = df_employ_fin.groupby(['graduation_type', "position_scope"])["shecodes_id"].count().unstack().fillna(0)

sns.set_style("whitegrid", {'axes.grid' : False})
plt.figure()
plt.rcParams["figure.figsize"] = [6.00, 8.00]
agg_employ_2.plot(kind='bar', stacked=True)
plt.title('Composition by position scope', fontweight='bold', color = 'black', fontsize='14', horizontalalignment='center')
plt.xticks(rotation=0, ha='center')
plt.xlabel('Graduation Type', fontweight='bold', color = 'black', fontsize='12', horizontalalignment='center')
plt.ylabel("Number of students", fontweight='bold', color = 'black', fontsize='12', horizontalalignment='center')
plt.legend(loc='upper left')
plt.savefig("fig_8.pdf")
plt.show()

<Figure size 432x576 with 0 Axes>


df_employ_fin_counts2 = df_employ_fin["position_scope"].value_counts().rename_axis("df_employ_fin2").reset_index(name="counts")
df_employ_fin_counts2["percent"] = (df_employ_fin_counts2["counts"] / df_employ_fin_counts2["counts"].sum()) * 100
df_employ_fin_counts2


fig_13 = df_employ_fin_counts2[["percent"]].plot(kind='barh', title ="SheCodes Users: Employment Type", figsize=(8, 10), 
                                        legend=True, fontsize=12, align='center', color='#86bf91', zorder=2, width=0.85)
sns.set_style("whitegrid", {'axes.grid' : False})
fig_13.set_xlabel("Percent (%)", fontsize=12, weight="bold", color="black")
fig_13.set_ylabel("Employment Type", fontsize=12, weight="bold", color="black")
fig_13.set_title("SheCodes Users: Employment Type", fontsize=14, color="black")
fig_13.set_yticklabels(["Full-time >35 hours per week", "Part-time >50%", "Part-time <50%", "Partially employed", 
                        "Freelance / Independent"])

fig_13.get_legend().remove()
plt.savefig("fig_13.pdf")
plt.show()


is_tech_dict = {0: "Non-tech Company", 1:"Tech Company"}
df_employ_fin["is_tech_company"].replace(is_tech_dict, inplace = True)


df_employ_fin


df_employ_fin_counts3 = df_employ_fin["is_tech_company"].value_counts().rename_axis("is_tech_company2").reset_index(name="counts")
df_employ_fin_counts3["percent"] = (df_employ_fin_counts3["counts"] / df_employ_fin_counts3["counts"].sum()) * 100
df_employ_fin_counts3


my_colors=["lightblue","lightgreen",
           "silver","green"]

fig_14 = df_employ_fin_counts3.groupby(["is_tech_company2"]).sum().plot(kind="pie", 
                                                         y="percent", 
                                                         figsize=(6,6),colors=my_colors, 
                                                         autopct="%1.1f%%",
                                                         explode=(0,0.15),
                                                         labels= ["Non-tech Company", "Tech Company"],
                                                         ylabel="",
                                                         title = "SheCodes Users: Employment type")
fig_14.figure.savefig("fig_14.pdf")
fig_14

<AxesSubplot:title={'center':'SheCodes Users: Employment type'}>


agg_employ_3 = df_employ_fin.groupby(['graduation_type', "is_tech_company"])["shecodes_id"].count().unstack().fillna(0)

sns.set_style("whitegrid", {'axes.grid' : False})
plt.figure()
plt.rcParams["figure.figsize"] = [6.00, 8.00]
agg_employ_3.plot(kind='bar', stacked=True)
plt.title('Is the student working in a tech company?', fontweight='bold', color = 'black', fontsize='14', horizontalalignment='center')
plt.xticks(rotation=0, ha='center')
plt.xlabel('Graduation Type', fontweight='bold', color = 'black', fontsize='12', horizontalalignment='center')
plt.ylabel("Number of students", fontweight='bold', color = 'black', fontsize='12', horizontalalignment='center')
plt.legend(loc='upper left')
plt.savefig("fig_9.pdf")
plt.show()

<Figure size 432x576 with 0 Axes>


df_educ_back_counts = df_educ_back["graduation_type"].value_counts().rename_axis("graduation_type").reset_index(name="counts")
df_educ_back_counts["percent"] = (df_educ_back_counts["counts"] / 
                                 df_educ_back_counts["counts"].sum())*100
df_educ_back_counts


my_colors=["lightblue","lightgreen",
           "silver","green"]

fig_10 = df_educ_back_counts.groupby(["graduation_type"]).sum().plot(kind="pie", 
                                                         y="percent", 
                                                         figsize=(6,6),colors=my_colors, 
                                                         autopct="%1.1f%%",
                                                         explode=(0,0.15),
                                                         labels= ["Complete", "Incomplete"],
                                                         ylabel="",
                                                         title = "Percentage of users who complete the program")
fig_10.figure.savefig("fig_10.pdf")
fig_10

<AxesSubplot:title={'center':'Percentage of users who complete the program'}>

	shecodes_id	id	userid_connect	branch_id	how_did_you_hear	where_I_heard_about_shecodes	why_shecodes	entry_date	submit_date	if_in_team	branch_name	short_name	city
0	5	5	7	12.0	3	None	19	11/11/2021	2021-11-11 08:08:36	team	Applied Materials	applied	2.0
1	6	6	8	13.0	None	None	None	31/12/2017	NaT	team	IBM	IBM	16.0
2	9	9	11	13.0	1, 2, 3, 4	None	5, 6, 7, 8, 9, 10, 13	31/12/2017	NaT	not_team	IBM	IBM	16.0
3	12	12	14	30.0	None	None	None	08/07/2018	NaT	team	Intel Jerusalem	Intel jlm	8.0
4	15	15	17	34.0	None	None	None	None	NaT	team	Ben Gurion University - Mon	BGU Mon	13.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
11605	32558	32558	28024	7.0	9	None	4, 19	29/10/2021	2021-10-29 18:46:40	not_team	Check Point	checkpoint	1.0
11606	32573	32573	28040	73.0	7	Joytunes	20	30/10/2021	2021-10-30 15:02:58	not_team	Monday Eve A Virtual	online branch	NaN
11607	32586	32586	28048	9.0	2	None	4, 19, 999	30/10/2021	2021-10-30 18:43:34	not_team	Google Campus Hacknight	google hacknight	1.0
11608	32624	32624	28076	9.0	1	None	4, 19	31/10/2021	2021-10-31 13:55:54	not_team	Google Campus Hacknight	google hacknight	1.0
11609	32644	32644	28085	52.0	5	None	19	31/10/2021	2021-10-31 18:36:45	not_team	Ebay	ebay	7.0

	id	shecodes_id	after_BA_work_difficulties	not_working_because_corona	working_terms_got_worse	hard_to_find_work	submit_date
0	14	17190	1.0	1	None	None	2021-02-26 05:26:21
1	15	17190	0.0	1	None	None	2021-02-26 06:07:24
2	16	17190	0.0	1	None	None	2021-02-26 09:49:09
3	22	28481	0.0	None	None	None	2021-03-03 10:12:56
4	24	11625	0.0	None	None	None	2021-03-03 10:30:52
...	...	...	...	...	...	...	...
3576	9093	32349	NaN	None	None	None	2021-11-22 12:30:20
3577	9094	32480	NaN	None	None	None	2021-11-22 12:31:24
3578	9095	32482	NaN	None	None	None	2021-11-22 12:35:58
3579	9096	32361	NaN	None	None	None	2021-11-22 12:38:03
3580	9098	32361	NaN	None	None	None	2021-11-22 12:42:11

	id	shecodes_id	after_BA_work_difficulties	not_working_because_corona	working_terms_got_worse	hard_to_find_work	submit_date	corona_victim
1897	4809	22119	NaN	NaN	0.0	0.0	2021-08-23 15:52:42	0
1899	4812	27907	NaN	NaN	0.0	0.0	2021-08-23 15:54:44	0
1901	4817	18568	NaN	NaN	0.0	0.0	2021-08-23 15:59:19	0
1896	4808	18196	NaN	NaN	0.0	0.0	2021-08-23 15:52:35	0
1912	4838	19165	NaN	NaN	0.0	0.0	2021-08-23 16:13:25	0
...	...	...	...	...	...	...	...	...
2633	6104	28428	NaN	NaN	2.0	0.0	2021-09-17 17:06:14	1
2647	6124	24218	NaN	NaN	0.0	1.0	2021-09-19 15:27:50	1
2648	6125	28984	NaN	NaN	2.0	0.0	2021-09-19 15:35:14	1
2592	6039	31191	NaN	NaN	0.0	1.0	2021-09-12 16:31:00	1
1790	4614	30050	NaN	NaN	0.0	1.0	2021-08-23 14:40:40	1

	corona_victim	counts	percent
0	0	1667	74.452881
1	1	572	25.547119

	id	shecodes_id	birthdate	family_status	kids_under_18	kids_6-9	kids_under_5	birth_country	aliya_year	city_grow	city	sector	religios_level	is_army_or_ns	is_army_or_ns_tech_unit	is_army_or_ns_tech_position	entry_date	submit_date
0	12502	6	7/8/1989	3.0	None	None	None	None	None	None	None	1	1.0	None	None	None	12/31/2017	NaT
1	11	74	19/12/1980	3.0	1	None	None	None	None	None	1127	1	1.0	None	None	None	06/03/2018	NaT
2	15	112	14/4/1989	1.0	0	None	None	1	None	1142	1248	1	1.0	2	0	0	11/01/2021	2021-02-19 22:26:39
3	11539	105	1/14/1988	3.0	None	None	None	None	None	None	None	1	1.0	None	None	None	7/24/2018	NaT
4	22578	110	25/09/1991	1.0	0	None	None	1	None	765	897	1	1.0	2	0	0	20/07/2021	2021-07-20 18:22:57
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9089	23615	31979	06/06/1993	1.0	0	None	None	1	None	1000	141	1	2.0	2	0	0	19/10/2021	2021-10-19 16:56:47
9090	23566	31930	10/03/1990	1.0	0	None	None	1	None	1248	1248	999	NaN	2	0	1	18/10/2021	2021-10-18 13:05:02
9091	22933	31313	30/09/1987	2.0	0	None	None	1	None	244	891	1	1.0	2	0	0	12/10/2021	2021-10-12 10:12:24
9092	23942	32295	03/09/1988	1.0	0	None	None	1	None	610	1248	1	1.0	2	1	1	26/10/2021	2021-10-26 18:01:50
9093	23539	31903	26/11/1998	1.0	0	None	None	1	None	817	817	1	2.0	1	None	0	17/10/2021	2021-10-17 21:41:46

Education¶

Employment¶

Percentage of graduates¶

	Tables_in_dbhfgcileg5zjx
0	corona_details
1	demographic_info
2	education
3	education_type
4	employment
5	graduates_followup
6	lessons_followup
7	lessons_mapping
8	users

	label	value	salesforce_id	label_english
0	רווקה	1	a0u4J000000HSKB	Single
1	במערכת יחסים	2	a0u4J000000HSKC	In a relationship
2	נשואה	3	a0u4J000000HSKD	Married
3	פרודה	4	a0u4J000000HSKE	Separated
4	גרושה	5	a0u4J000000HSKF	Divorcee
5	חיה בנפרד	6	a0u4J000000HSKG	Live separately
6	אלמנה	7	a0u4J000000HSKH	Widow
7	לא מעוניינת לענות	999	a0u4J000000HSKI	Not interested in answering

	family_status	counts	percent
0	1.0	4484	49.448610
1	3.0	3011	33.204676
2	2.0	1055	11.634318
3	999.0	286	3.153948
4	5.0	200	2.205558
5	4.0	19	0.209528
6	7.0	8	0.088222
7	6.0	5	0.055139

	label	value	label_english_2
0	לא ידוע	0	Unknown
1	ישראל	1	Israel
2	מזרח אירופה ובריהמ לשעבר"	2	Eastern Europe and the former Brahms
3	מערב אירופה	3	Western Europe
4	אסיה והמזרח	4	Asia and the East
5	אפריקה	5	Africa
6	צפון אמריקה	6	North America
7	מרכז ודרום אמריקה	7	Central and South America
8	אוסטרליה	8	Australia
9	מזרח התיכון	9	Middle East

	birth_country	counts	percent
0	1	5369	80.134328
1	2	836	12.477612
2	6	161	2.402985
3	3	144	2.149254
4	7	87	1.298507
5	5	37	0.552239
6	4	36	0.537313
7	8	15	0.223881
8	9	8	0.119403
9	0	7	0.104478

	label	value	religious_level
0	חילונית, לא דתיה	1	Secular, not religious
1	מסורתית	2	Traditional
2	דתיה	3	Religious
3	מסורתית- לא כל כך דתיה	4	Traditional - not so religious
4	מסורתית- דתיה	5	Traditional - religious
5	אורתודוכסית	6	Orthodox
6	חרדית	7	Haredi
7	לא מעוניינת לענות	999	Not interested in answering
8	אחר	9999	Other

	religious_level2	counts	percent
0	1.0	5269	65.355991
1	2.0	773	9.588192
2	999.0	612	7.591168
3	3.0	466	5.780203
4	7.0	284	3.522699
5	4.0	250	3.100968
6	6.0	230	2.852890
7	5.0	96	1.190772
8	9999.0	53	0.657405
9	0.0	29	0.359712

	id	shecodes_id	birthdate	family_status	kids_under_18	kids_6-9	kids_under_5	birth_country	aliya_year	city_grow	city	sector	religios_level	is_army_or_ns	is_army_or_ns_tech_unit	is_army_or_ns_tech_position	entry_date	submit_date	Age
0	12502	6	1989-07-08	3.0	None	None	None	None	None	None	None	1	1.0	None	None	None	12/31/2017	NaT	33.0
1	11	74	1980-12-19	3.0	1	None	None	None	None	None	1127	1	1.0	None	None	None	06/03/2018	NaT	42.0
2	15	112	1989-04-14	1.0	0	None	None	1	None	1142	1248	1	1.0	2	0	0	11/01/2021	2021-02-19 22:26:39	33.0
3	11539	105	1988-01-14	3.0	None	None	None	None	None	None	None	1	1.0	None	None	None	7/24/2018	NaT	34.0
4	22578	110	1991-09-25	1.0	0	None	None	1	None	765	897	1	1.0	2	0	0	20/07/2021	2021-07-20 18:22:57	31.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9089	23615	31979	1993-06-06	1.0	0	None	None	1	None	1000	141	1	2.0	2	0	0	19/10/2021	2021-10-19 16:56:47	29.0
9090	23566	31930	1990-10-03	1.0	0	None	None	1	None	1248	1248	999	NaN	2	0	1	18/10/2021	2021-10-18 13:05:02	32.0
9091	22933	31313	1987-09-30	2.0	0	None	None	1	None	244	891	1	1.0	2	0	0	12/10/2021	2021-10-12 10:12:24	35.0
9092	23942	32295	1988-03-09	1.0	0	None	None	1	None	610	1248	1	1.0	2	1	1	26/10/2021	2021-10-26 18:01:50	34.0
9093	23539	31903	1998-11-26	1.0	0	None	None	1	None	817	817	1	2.0	1	None	0	17/10/2021	2021-10-17 21:41:46	24.0

	id	shecodes_id	type	diploma_status	institute	start_year	end_year	first_discipline	first_faculty	second_discipline	second_faculty	course_name	entry_date	start_date	end_date	submit_date	studies_after_shecodes
0	7261	6	3.0	13.0	3	0.0	2015.0	0.0	2000	0	0.0	None	31/12/2017	None	None	None	None
1	1562	112	3.0	13.0	3	2012.0	2015.0	614.0	280	0	810.0	None	30/06/2019	None	None	None	None
2	7901	112	3.0	23.0	2	2015.0	2017.0	614.0	810	0	280.0	None	30/06/2019	None	None	None	None
3	11930	112	1.0	2.0	None	0.0	0.0	13.0	None	0	0.0	None	30/06/2019	None	None	None	None
4	48054	112	3.0	13.0	3	2012.0	2015.0	9000.0	614	None	203.0	None	14/10/2020 16:16:44	2012-10-01	2015-08-01	1602692201	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
28700	66113	31313	3.0	13.0	2	2010.0	2015.0	6036.0	6006	None	NaN	None	12/10/2021 10:12:24	2010-10-24	2015-03-25	1634033544	None
28701	66114	31313	1.0	2.0	None	NaN	NaN	33.0	None	None	NaN	None	12/10/2021 10:12:24	None	None	1634033544	None
28702	69876	32295	2.0	3.0	999	2007.0	2009.0	19.0	None	None	NaN	None	26/10/2021 18:01:50	2007-03-11	2009-06-14	1635271310	None
28703	69877	32295	1.0	2.0	None	NaN	NaN	13.0	None	None	NaN	None	26/10/2021 18:01:50	None	None	1635271310	None
28704	68209	31903	1.0	2.0	None	NaN	NaN	13.0	None	None	NaN	None	17/10/2021 21:41:46	None	None	1634506906	None

	id	shecodes_id	type	diploma_status	institute	start_year	end_year	first_discipline	first_faculty	second_discipline	second_faculty	course_name	entry_date	start_date	end_date	submit_date	studies_after_shecodes
8154	10681	15799	2.0	3.0	71	2018.0	0.0	0.0	None	0	0.0	None	01/01/2019	None	None	None	None
1612	10049	6775	2.0	3.0	8	2015.0	0.0	0.0	None	0	0.0	None	01/01/2019	None	None	None	None
7658	10669	15171	2.0	3.0	64	2015.0	0.0	0.0	None	0	0.0	None	01/01/2019	None	None	None	None
7980	10053	15569	2.0	3.0	8	1991.0	0.0	0.0	None	0	0.0	None	01/01/2019	None	None	None	None
8581	10623	15507	2.0	3.0	36	2016.0	0.0	0.0	None	0	0.0	None	01/01/2019	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
26464	51847	27331	2.0	3.0	9	2018.0	2019.0	3.0	None	None	NaN	None	31/12/2020 05:49:33	2018-12-16	2019-12-16	1609393771	None
22914	51872	25231	2.0	2.0	8	2020.0	2021.0	3.0	None	None	NaN	None	31/12/2020 08:14:30	2020-09-01	2021-04-01	1609402467	None
23380	51908	25574	2.0	3.0	999	2020.0	2020.0	19.0	None	None	NaN	None	31/12/2020 09:14:19	2020-02-01	2020-07-31	1609406057	None
23753	52014	27391	2.0	3.0	999	2011.0	2013.0	12.0	None	None	NaN	None	31/12/2020 14:26:30	2011-04-01	2013-03-31	1609424788	None
23455	52057	27406	2.0	3.0	999	2016.0	2016.0	8.0	None	None	NaN	None	31/12/2020 17:43:29	2016-01-01	2016-07-28	1609436608	None

	id	userid_connect	track_id_connect	badge_id	graduation_type	date_enrolled	date_graduated
0	2365	1343	11	21	4	2018-04-08 16:07:21	2018-12-02 21:07:55
1	2366	8701	11	21	4	2018-05-02 15:32:11	2018-11-14 18:34:02
2	2367	12120	11	21	4	2018-07-23 05:49:02	2018-11-25 18:49:01
3	2368	11	11	27	3	2016-08-01 10:44:05	2018-10-22 10:23:31
4	2369	50	11	27	3	2016-07-25 12:22:38	1970-01-01 00:00:01
...	...	...	...	...	...	...	...
2620	14061	26081	183	276	2	2021-08-03 07:34:07	2021-11-15 17:37:09
2621	14062	26298	183	276	2	2021-08-03 07:36:54	2021-11-15 18:39:53
2622	14070	26443	183	276	2	2021-08-03 07:36:57	2021-11-16 18:04:44
2623	14076	26379	183	276	2	2021-08-03 07:36:55	2021-11-18 14:35:23
2624	14079	26455	183	276	2	2021-08-03 07:36:57	2021-11-19 13:29:45

	id	userid_connect	track_id_connect	badge_id	graduation_type	date_enrolled	date_graduated
190	6577	50	11	27	3	2016-07-25 12:22:38	2018-11-15 14:52:13
189	6576	11	11	27	3	2016-08-01 10:44:05	2018-11-15 14:52:13
1530	12600	117	84	1	2	2016-08-02 15:53:02	2019-01-08 18:44:17
655	9137	139	11	71	2	2016-08-02 15:57:56	2020-04-01 17:34:09
680	10773	141	11	71	2	2016-08-02 17:01:35	2020-10-07 20:47:00
...	...	...	...	...	...	...	...
2555	13721	26655	183	275	4	2021-08-06 08:22:41	2021-10-02 01:44:04
2553	13693	18665	183	276	2	2021-08-07 21:09:53	2021-09-28 21:40:03
2565	13776	13575	183	276	2	2021-08-09 15:01:53	2021-10-11 16:09:38
2619	14058	26714	183	276	2	2021-08-15 17:37:45	2021-11-14 18:07:12
2534	13910	22031	177	254	4	2021-10-28 06:29:02	2021-10-28 06:30:07

	shecodes_id	userid_connect	track_id_connect	graduation_type	date_enrolled	date_graduated	type_1.0	type_2.0	type_3.0	type_4.0	status
0	5	7	11	Incomplete	2018-04-08 16:50:46	2019-07-02 18:49:20	1	1	1	1	Higher education with tech background
1	9	11	11	Complete	2016-08-01 10:44:05	2018-11-15 14:52:13	1	0	0	0	High School only
2	88	90	11	Incomplete	2018-07-29 11:33:31	2020-05-19 07:26:57	1	0	0	0	High School only
3	112	115	144	Incomplete	2021-01-27 06:52:49	2021-02-20 21:59:00	1	0	1	0	Higher Education only
4	114	117	84	Incomplete	2016-08-02 15:53:02	2019-01-08 18:44:17	1	0	1	0	Higher Education only
...	...	...	...	...	...	...	...	...	...	...	...
1488	31036	26531	183	Incomplete	2021-08-03 07:36:58	2021-11-02 19:50:04	1	0	1	0	Higher Education only
1489	31067	26113	183	Incomplete	2021-08-03 07:34:08	2021-09-28 13:01:08	1	0	1	0	Higher Education only
1490	31071	26455	183	Incomplete	2021-08-03 07:36:57	2021-11-19 13:29:45	1	0	1	0	Higher Education only
1491	31158	26475	183	Complete	2021-08-03 07:36:57	2021-09-26 14:34:04	1	0	1	0	Higher Education only
1492	31225	26497	183	Incomplete	2021-08-03 07:36:58	2021-10-10 07:48:06	1	1	0	0	High school with tech background

	currently_employed2	counts	percent
0	Unemployed	867	58.187919
1	Employed	623	41.812081

	id	shecodes_id	type	diploma_status	institute	start_year	end_year	first_discipline	first_faculty	second_discipline	second_faculty	course_name	entry_date	start_date	end_date	submit_date	studies_after_shecodes
2116	14641	8113	1.0	NaN	None	0.0	0.0	42.0	None	0	0.0	None	01/01/2018	None	None	None	None
7536	13751	8105	1.0	NaN	None	0.0	0.0	2.0	None	0	0.0	None	01/01/2018	None	None	None	None
3112	13748	8092	1.0	NaN	None	0.0	0.0	2.0	None	0	0.0	None	01/01/2018	None	None	None	None
2113	13762	8044	1.0	NaN	None	0.0	0.0	2.0	None	0	0.0	None	01/01/2018	None	None	None	None
2095	13755	8037	1.0	NaN	None	0.0	0.0	2.0	None	0	0.0	None	01/01/2018	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5699	52056	13089	1.0	2.0	None	NaN	NaN	47.0	None	None	NaN	None	31/12/2020 17:42:04	None	None	1609436517	None
23456	52058	27406	1.0	2.0	None	NaN	NaN	13.0	None	None	NaN	None	31/12/2020 17:43:30	None	None	1609436608	None
23818	52068	27410	1.0	2.0	None	NaN	NaN	76.0	None	None	NaN	None	31/12/2020 18:17:55	None	None	1609438672	None
23569	52070	27411	1.0	2.0	None	NaN	NaN	33.0	None	None	NaN	None	31/12/2020 19:09:51	None	None	1609441789	None
23392	52079	27415	1.0	2.0	None	NaN	NaN	33.0	None	None	NaN	None	31/12/2020 20:12:48	None	None	1609445565	None

	id	shecodes_id	type	diploma_status	institute	start_year	end_year	first_discipline	first_faculty	second_discipline	second_faculty	course_name	entry_date	start_date	end_date	submit_date	studies_after_shecodes
23505	52094	27420	4.0	1.0	None	2012.0	2013.0	NaN	None	None	NaN	רואת חשבון	01/01/2021 09:15:02	2012-01-01	2013-01-01	1609492498	None
23462	52097	27421	4.0	1.0	None	2013.0	2015.0	NaN	None	None	NaN	רואת חשבון	01/01/2021 09:15:05	2013-09-01	2015-12-31	1609492498	None
22860	52100	27422	4.0	1.0	None	2019.0	2019.0	NaN	None	None	NaN	קופירייטינג ושיווק דיגיטלי	01/01/2021 09:24:35	2019-01-01	2019-05-01	1609493072	None
23374	52156	27442	4.0	1.0	None	2018.0	2019.0	NaN	None	None	NaN	קאוצ'ינג באונ' חיפה	01/01/2021 17:20:49	2018-11-08	2019-07-26	1609521646	None
26121	34162	23245	4.0	1.0	None	2017.0	2019.0	NaN	None	None	NaN	מנהלת חשבונות	01/03/2020 00:11:29	2017-12-22	2019-12-03	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
22386	39008	23879	4.0	1.0	None	2012.0	2013.0	NaN	None	None	NaN	ספורט-תרפיסטית	None	2012-10-02	2013-07-30	1588668996	None
17288	39002	23905	4.0	1.0	None	2003.0	2007.0	NaN	None	None	NaN	קולנוע מביה"ס סם שפיגל	None	2003-10-01	2007-05-31	1588668996	None
21236	39000	23907	4.0	1.0	None	2015.0	2020.0	NaN	None	None	NaN	משחק	None	2015-10-13	2020-08-01	1588668996	None
16995	38994	23934	4.0	1.0	None	1991.0	1991.0	NaN	None	None	NaN	קורס תכנות COBOL ו- BASIC, בתוספת תרשים זרימה	None	1991-08-05	1991-12-20	1588668996	None
18811	38991	23955	4.0	1.0	None	2017.0	2018.0	NaN	None	None	NaN	אימון אישי - קאוצרית	None	2017-07-16	2018-04-22	1588668996	None

	shecodes_id	id	diploma_status	start_year	end_year	first_discipline	second_faculty	type_1.0	type_2.0	type_3.0	type_4.0
0	5	263812	27.0	6046.0	6022.0	8014.0	0.0	1	1	1	1
1	6	7261	13.0	0.0	2015.0	0.0	0.0	0	0	1	0
2	9	15364	0.0	0.0	0.0	111.0	0.0	1	0	0	0
3	12	7229	13.0	0.0	0.0	0.0	0.0	0	0	1	0
4	21	92063	14.0	2020.0	2023.0	9013.0	0.0	1	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...
8632	32558	140615	3.0	2016.0	2020.0	116.0	0.0	1	0	0	1
8633	32573	140761	25.0	2008.0	2015.0	9012.0	0.0	1	0	1	0
8634	32586	70429	2.0	0.0	0.0	27.0	0.0	1	0	0	0
8635	32624	141129	14.0	2019.0	2022.0	9076.0	624.0	1	0	1	0
8636	32644	141345	15.0	2010.0	2014.0	9013.0	0.0	1	0	1	0

	id	shecodes_id	currently_employed	position_scope	is_tech_company	company_name	is_tech_position	start_job_date	end_job_date	employment_domain	employment_role	job_rank	seniority	company_type	not_working_reason	entry_date	submit_date
0	2	6	1.0	3	1.0	Google	1.0	None	None	11	None	None	5	7	None	31/12/2017	None
1	19	74	0.0	None	0.0		0.0	None	None	None	None	None	None	None	None		None
2	28	112	0.0	None	NaN		NaN	None	None	None	None	None	None	None	None	30/06/2019	None
3	24321	112	0.0	3	0.0	משרד רה״מ	0.0	13/02/2020	None	19	None	None	3	4	None	14/10/2020	1602692201
4	24322	112	0.0	3	1.0	TASC	0.0	21/07/2019	09/02/2020	24	None	None	4	1	None	14/10/2020	1602692201
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18163	40717	31313	0.0	3	0.0	אקולוג	0.0	13/03/2016	21/01/2018	11	None	None	4	1	None	12/10/2021	1634033544
18164	44449	32295	1.0	3	1.0	-	1.0	23/12/2019	None	4	999	6	5	5	None	26/10/2021	1635271310
18165	44450	32295	0.0	None	NaN	None	NaN	None	None	None	None	None	None	None	None	26/10/2021	1635271310
18166	42522	31903	0.0	None	NaN	None	NaN	None	None	None	None	None	None	None	999	17/10/2021	1634506906
18167	42523	31903	0.0	None	NaN	None	NaN	None	None	None	None	None	None	None	None	17/10/2021	1634506906

	id	shecodes_id	currently_employed	position_scope	is_tech_company	company_name	is_tech_position	start_job_date	end_job_date	employment_domain	employment_role	job_rank	seniority	company_type	not_working_reason	entry_date	submit_date
6177	9139	16507	0.0	None	NaN		NaN	None	None	None	None	None	None	None	None		None
4173	180	1004	1.0	4	1.0	תעשייה אווירית	1.0	None	None	35	1	None	4	7	None		None
4176	5981	12617	0.0	None	0.0		0.0	None	None	None	None	None	None	None	None		None
4181	4724	11220	1.0	3	0.0	צהל	0.0	None	None	None	None	None	5	4	None		None
4183	7099	13898	1.0	None	NaN		NaN	None	None	None	None	None	None	None	None		None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1343	2075	8213	0.0	None	0.0		0.0	None	None	None	None	None	None	None	None	9/1/2018	None
1909	3291	9514	1.0	3	0.0	אלביט	0.0	None	None	11	None	None	5	7	None	9/1/2018	None
1918	3307	9531	1.0	3	0.0	פירון חיפה חברת עורכי דין	0.0	None	None	3	None	None	6	7	None	9/1/2018	None
925	1193	6617	1.0	3	0.0	הדרכה ישירה	0.0	None	None	18	None	None	5	2	None	9/1/2018	None
759	318	2053	1.0	5	NaN		NaN	None	None	None	None	None	None	None	None	None	None

	status2	counts	percent
0	Higher Education only	809	54.186202
1	Higher education with tech background	488	32.685867
2	High School only	124	8.305425
3	Other	42	2.813128
4	High school with tech background	30	2.009377

	shecodes_id	currently_employed	is_tech_company	is_tech_position	graduation_type	position_scope
0	129	0.0	NaN	NaN	3	None
1	12364	1.0	0.0	0.0	2	4
2	13585	0.0	NaN	0.0	2	None
3	13715	0.0	NaN	0.0	3	None
4	13746	1.0	NaN	1.0	2	3
...	...	...	...	...	...	...
1534	9202	1.0	0.0	0.0	2	3
1535	9122	0.0	0.0	0.0	4	None
1536	9112	0.0	0.0	0.0	3	None
1537	5197	1.0	0.0	1.0	4	3
1538	9454	0.0	0.0	0.0	4	None

	df_employ_fin2	counts	percent
0	Full-time (over35 hours per week)	597	70.152761
1	Part-time (over 50%)	88	10.340776
2	Part-time (less than 50%)	73	8.578143
3	Partially employed	70	8.225617
4	Freelance / Independent	23	2.702703